Ever been told that you're not ready just because you haven't done it before?

Well, that's the trap so many leaders get stuck in.

The cruelest ceiling isn't glass.

It's being pigeonholed in your specialty and never trusted with anything more.

If you're brilliant in your craft but invisible when the bigger decisions get made, you know of exactly the wall that we're talking about today.

I did run into a conversation which I'm sure many, many people have experienced, which was, well, we don't feel comfortable with you doing that because you haven't done it yet.

Wow.

How am I going to do it if someone doesn't give me a chance to do it?

But Dan Faulkner didn't stay boxed in.

It takes time.

I started out as a researcher years ago out of university, researching speech recognition and text to speech synthesis.

CEO of SmartBear, a company trusted by literally millions of developers deep in the AI world.

This episode will show you the real difference between the experts who stall out and the experts who lead.

And.

And it's not talent, it's something else entirely.

You have to have enough humility to be able to go in and say, I don't know what I'm doing.

I'm going to learn this and kind of back yourself to move quickly.

So here are the mindset shifts, the career moves, and the leadership lessons that took Dan from research labs to the C suite and how you can apply them too.

You're the steward of your own career.

If you're not driving it, you're going.

To get the default welcome back to lead the team.

I'm your host, Ben Fanning, and this conversation that you're going to hear is meant to challenge, inspire and ripple out.

It's not just a podcast.

It's a positive movement to build better leaders.

And you can help by taking just 10 seconds to rate and follow on Apple, Spotify and YouTube and drop a quick review over on Apple.

This helps more bold leaders discover the show and keeps the mission alive.

Enjoy.

Too many talented people get pigeonholed.

They're brilliant at their first craft, coding, research, engineering, but still carry the label just the tech person.

And the irony is, the better they get technically, the harder sometimes it is to break out.

And for so many, that's a career ceiling that they never escape.

But some do break through and they figure out how to move from being the expert in the room in the weeds to becoming the leader in the room.

And when they do, they don't just change their own career, they actually change the companies and the teams Today, Dan Faulkner is one of those leaders.

He started out deep in the AI world and built products to scale.

And today he's actually the CEO of Smart Bear, a company trusted by literally millions of developers and testers worldwide.

And along the way he's watched AI itself evolve from rules based experiments to generating huge breakthroughs and learned quite a few lessons along the way that every person, every technical expert in the world can apply.

So today we're going to dig into a couple really interesting things.

Breaking the ceiling.

What does it take to really move from expert to leader?

And why do so many get stuck along the way?

And then AI's big transformation and how AI itself has changed since the early days and what lessons leaders can take from its evolution.

Dan, my friend, welcome to the show.

Thank you, Ben.

It's a pleasure to be here.

So what does AI, a researcher or how does a researcher end up running a global company?

It, it takes time.

It.

I started out as a researcher years ago out of university, researching just the stuff I had studied in university, which was speech recognition and text to speech synthesis.

So all those voices that you hear on Amazon, Alexa or Siri or now ChatGPT, I was working on that a long time ago.

Wow.

And you have the accent to prove it.

Like doesn't every Siri.

Everyone's changing their name to their English or Australian accent.

Yeah, that's right.

So it's it, it, it's a very different technology to what it was today, but that was how I started and I was very fortunate to.

There weren't that many companies who were working in that space at the time.

And I was very fortunate that as I graduated from master's degree, there was an MIT spin out called speechworks that was looking for people who knew anything about this and I was one of them.

So that's what brought me to the States.

But after a few years I became very curious about why are people actually paying for this and how are they really using it and why is it actually as valuable as it seems to be?

And that piqued my interest to such an extent that, that I kind of raised my hand and said I want to go and start digging into those kinds of questions.

And so I guess the first answer to the how is you need to be curious.

The second answer to the how is you need someone inside the company who's going to actually believe in you and sponsor you making that kind of lateral change.

If you don't have that, I think it'll be very difficult to leave a company and change your function, you need to have enough internal credibility that someone inside the company will say, yes, I think that, that you'd be good enough to, to be able to make that.

That change.

It sounds like you had a reputation.

Is like the tech guru in the room.

And.

But no one was saying, hey, Dan's.

Dan would make a great CEO one day.

They're probably saying, Dan's going to be a great specialist.

Right.

Or maybe, maybe he'll lead a tech team.

So that's.

That's right.

What did you do though?

What?

Because that's a big thing to break out of.

And also I feel like experts get so good and so comfortable being the expert.

And when you're a CEO, you can't know the answer to everything because you get way too many other different fields under you.

Right.

To be the expert.

So why make that jump in the first place?

And then how'd you do it?

I'm just very curious and I think I tend to think about like the whole problem.

And of course, when you're in any particular function, you're thinking about part of the problem and there's nothing as valuable as context.

So I was just curious about what's the business context that we're building these in.

And it just got me interested in that.

And then I think a very important thing is I made no assumptions that any of my competencies from the research team would translate into.

I actually moved into a product marketing role.

That was the first move I took very big.

And so I did a. I did a marketing degree online in my spare time.

I was like, well, I don't know anything about marketing, but I'm interested in this.

So I actually went and did a marketing degree online to try and learn the basics.

Wow.

So a couple things come to mind there.

Number one, you were curious about things outside of your own world.

So it's one thing if you're like a data scientist or engineer and you're really curious about engineering and data type things and you're going deeper, deeper, deeper.

But you were getting curious wider.

And I love the idea of I'm getting curious about the wider world of business, just not my expertise.

When you started asking questions and getting curious about that area, was it weird?

Was it like, was it humbling to kind of show up as like the third grader in marketing?

I'm like, okay, I'm going to go learn about marketing and take a course in marketing.

Like you're kind of starting back as the freshman year when you'd already had your Ph.D. so to speak.

Yes, but it depends on your mindset.

So it would only be humbling if you thought you were going to be good at it.

I think you have to have enough humility to be able to go in and say, I don't know what I'm doing, I'm going to learn this and kind of back yourself to be able to, to move quickly and learn quickly.

And it turned out to be a good move.

I actually ended up leading product marketing for one of our business units after a couple of years and then moved over into product management because I increasingly felt like that's where the good stuff is.

That's where you actually start to define where something is going and have your hands on all of the levers, the legal, the financial, the technical, the strategic.

And so I found product management very alluring.

And so I hopped over again from product marketing to product management.

Before we get off the marketing piece, did you decide to self fund your own marketing course or did you say, hey, there's something my, my company could compensate me for?

Actually, I was fortunate.

The company I worked at at the time, which was called Nuance Communications, which ended up being acquired by Microsoft a couple of years ago, they would partially fund higher education.

So we both chipped in.

So.

Okay, great.

So like a collaborative education approach.

Yeah.

Did you identify?

You're like, I want to learn about marketing, I'm going to go enroll in this.

Or did they say, Dan, we want you to go do this?

No, no, no, no.

That was me.

Okay.

That was, that was sort of, that was just some.

Like I said, it's not humbling if, you know, you don't know anything about it.

I was just thinking, well, if I'm going to do this, I better find out something about it.

So, okay, I enrolled myself in a, a master's course at a UK university and just did that online.

Really, really important for people to be recognizing here.

I mean, Dan's CEO now.

But you made those investments of like you may think, well, they both chipped in.

Wow.

He had to spend his own money, learn about marketing.

But what's more, what's more expensive?

Expensive than the money is your own time.

Right.

Because you're doing a day job in a very rap in a company doing a lot of big things.

And here you are taking your own time to go learn about marketing, but you're willing to make that investment.

Absolutely.

Yeah.

Wow.

And then you get a product and what happens?

Because that's a whole nother kid and caboodle.

Right.

Just because you understand data.

Just because you understand marketing doesn't mean you understand product design.

Right.

That's a whole nother.

Yeah.

I mean I, again, you'll hear me say this a few times.

I was fortunate and I think luck is important really in anyone's trajectory, you know, good or bad usually.

And I was fortunate to work at a company that was highly technical.

I mean, the company was doing groundbreaking research in speech recognition, text to speech synthesis, natural language understanding.

And so we weren't, I didn't need to be able to, you know, design a good, you know, user interface or we weren't doing high velocity packaged products.

We were, they were highly technical platform based products.

So it was a good space for someone who'd been in research to move into product management.

You, it was a real advantage to be able to understand how the technology really worked within that role.

So kind of having the combination of the technical background and some marketing under my belt, it was a good foundation to get into product management.

And when I did that, the main area that I felt I wanted to expand was my financial acumen.

So there I just did internal networking.

I would go and park myself with the FP and a team and say, all right, you got to explain this P and L to me.

You need to explain which bits in here matter, which are the levers that I could actually pull and which are the ones I don't pull and can't control.

So I just got up to speed on that so that I could start thinking about my strategy in terms of the financial outcome, because it has to have a financial outcome.

That reminds me of the whole Jim Rohn quote about, you are the average of the people you spend the most time with.

And the greatest life hack of all time is to go find people you want to learn from and be more alike and just go hang out with them.

But it also requires you checking your ego out the, at the door because you're showing up in the finance room.

Right.

Because realizing you don't know all the bottom line components of the, of the spreadsheet or of the financial reporting and you're, you're there to learn the language.

Yeah.

And I mean, I think that holds true today.

I've always kind of subscribed to the idea that the more you know, the more you realize you don't know.

And so you just have to remain open to being educated by people who know more about anything than you do.

And I think that also applies and probably applies as much as any other context when you're the CEO.

Yeah.

So, so good people don't think about, hey, gonna go learn about product.

But you have a really learned there to learn about finance.

Like, how are we building the financial bones of this service or product to make it work?

And the good news, bad news is, y', all, I was a finance major.

And you know, we know where I learned finance.

Doing finance, right?

Being with finance people in the business machine.

And that's where you really learn the language now what was it?

So you really.

The way I think about your career too is you got known on the data research side like that.

Like you've, like if Dan walked in the board, in the boardroom, like, okay, there's our data expert.

Now you've got another problem because now here comes Dan, he's our product guy.

What did you do to get known as the CEO guy?

So the company I was at, I was there for quite a long time.

Nuance was, was good to me over the years because they gave me the opportunity to try these different things.

And it was a business unit structure, so they had general managers who would run certain product lines.

And after a few years running the product management function inside one of the main divisions, I was given the opportunity to become the general manager of one and then a couple of the product lines within the company.

And that is kind of the beginnings.

It's the skeleton of a CEO job.

You have, you're responsible for a P and L. You have a sales team that's supporting your business.

You've got to manage and think about most of the functions.

What's done for you as a service for free is kind of the GNA functions and all the back office stuff, but the kind of the directly business facing functions, you've got to orchestrate them.

And so that was, that was the step from product management to general management within that business was really the thing that I think that gave me the foundation where from which I might become a candidate to be a CEO in the future.

Well, so is your career like, hey, I'm curiosity in these areas, I'm developing, I'm growing.

Did you raise your hand for these GM positions and say, and yeah, here, Mikey, I want to be a gm, I want this experience.

Or was it more of someone knocking on your door and saying, Dan, you ever thought about being a general manager for our business units?

Like, how did it play out there?

No, I raised my hand.

I would sort of after a while observe the businesses and think, yeah, I could do that.

I think I'm interested in that.

And so yes, I certainly let people know that I'd be interested in doing that.

Can you take us back to one of those conversations?

Even if it's like a, like, like when that actually happened or maybe pulling from different ones, like take us back to the conversation and like you walk in the room, how do you express.

It's such a bold move.

I think I should be leading one of the business units around here or I would like to do it, or take us back and just kind of play out how that would go.

So again, I think a huge amount of it is forging strong relationships and good relationships with people.

360 People who work for you, who you work with, who you work for.

And of course, over time people ask career questions, it will come up in conversation.

What I'd really like to do one day is be a general manager and run one of the business units.

And I can remember once actually probably expressing some frustration that, you know, I feel like, you know, we're bringing folks in from the outside and I could have done that.

And I did run into a conversation which I'm sure many, many people have experienced, which was, well, we don't feel comfortable with you doing that because you haven't done it yet.

Which of course makes you say, well, how am I going to do it if someone doesn't give me a chance to do it?

Yes.

And then eventually they did.

There was a relatively small business line and the person who'd been running that business left and they said, you know what, why don't you run that, see how that goes.

And I did a decent enough job with that one to get broader opportunities.

Yes.

It's so frustrating when you're working inside an organization, you've had success and people believe that you're only qualified to do a slightly more advanced version of what you're already doing.

Yes, yes.

And I can't emphasize enough the importance of having people who believe in you, who will sponsor you into those roles that you haven't had before.

The idea that, you know, you could found your own company and become self made.

And I have a huge amount of respect for people who do that.

But if you're growing your career within a bigger company, you're dependent on people who are willing to stick their neck out and say, I think we should take a chance on that person, play.

Out a conversation with that on the sponsorship front.

So you might have people on here like, yeah, sponsorship will be nice, but I don't know where to start.

Even having that conversation makes me break out in hives.

Oh, you mean you Mean sponsoring someone or asking them to sponsor.

I'm thinking asking somebody proactively to be your sponsor.

And yes, I'm thinking about, you know, expert level, data level, researchers, but also people mid level career or even people that are VPs, directors out there who are like, yeah, I want to do something bigger, but I don't really have a sponsor in this company.

Yeah, I mean the first thing to do is establish relationships with people and generally that's going to be people who you genuinely like and who genuinely like you.

I truly think that it, like a lot of this comes from friendship.

And when you work somewhere for a long time, you do become friends with people.

You have to have a track record of doing your job well, the job you're in well.

And then I, I don't think it takes that much more than letting it be known that you're interested, saying, you know, I like what I do.

I think I could do more that will invite the question, well, what more would that be?

What kind of thing?

For some people it might be, well, I want to get experience in management or I want to move up in the organization.

For others it might be, I want to go broader, which was my case.

And as soon as you.

That is a zero consequence discussion.

There's no manager I've ever met who's going to say, I wish I didn't have people who were a bit ambitious.

I wish I didn't have people who, you know, why can't you just sit there and do that forever?

Yeah.

And if they think that they need to rethink that thought, like if you're a manager, that's like, I really want to keep everybody on my team exactly where they are.

That is a bad idea.

Yeah, that would be a bad manager.

So it's something I look out for today is people who are raising their hands, who are volunteering to do stuff, who want to get involved in other stuffs in other stuff.

But you got to be good at what you're doing now.

You have to have established that internal credibility.

And, and then I found that, you know, opportunities do arise over time, so you need a little bit of patience.

And then someone will think of you and they'll, they'll go, oh yeah.

This is the kind of thing that so and so might be interested in.

Dan, why do so many brilliant people stay stuck as the tech guy or the data guy?

Well, I think a lot of them aren't stuck.

A lot of them really like that and don't want to change.

But for the people who do Stay stuck.

There can be a few reasons from a personal perspective.

At an individual level, they may not feel confident enough to say, hey, I'd like to do something else.

People worry that it's going to make it sound like they don't want their current job or they're not going to be satisfied with their current job or their flight risk.

And I think if people feel anxious about that, about their own job security, then they're less likely to have that conversation.

So you need an environment, not necessarily a corporate wide environment, but at least a localized management environment where you can have that conversation safely and then you need the confidence, the self confidence to do it.

So if you don't have that safe environment and you don't have that self confidence, then you're never going to initiate it.

And I have seen a lot of people who start career conversations by saying, well, I've been doing this for two years now.

What is the company's vision for me?

And that is not a good question to ask your manager because it implies that your managers, that's your manager's job.

Your managers should be thinking about career development in general.

But you're the steward of your own career and if you're not driving it, you're going to get the default.

Something might happen, you might be lucky, but you should be driving.

That's cold hard advice.

That's cold hearted and so true.

Like out for 10 years of my career, that's what I thought, my boss to read my mind and my company should have a vision for me as an employee.

And it did nothing but lead to extreme frustration and burnout.

What if my vision for you as an employee is wildly different from your vision for you as an employee?

What am I meant to do if you haven't even told me your vision for you as an employee?

Yeah, it's just a reality.

We have to take more ownership and put some real thought into our own careers of what we want to see from it.

Yeah.

So I want to think about your background and I know we have a ton of time, but I've got, I want to.

You've been with the AI world for a long time.

Has AI really changed or is it just being hyped differently?

It's wildly changed.

It's so different.

The techniques and tools are different.

The techniques and tools that we used to use were very labor intensive.

They're still extremely good for certain types of problems.

But the general MO would be gather a very, very large amount of data, which was generally a fairly expensive thing to do because it didn't exist already.

Label it meticulously manually train models and then create predictive models that would be very good at doing something very narrow.

So speech recognition started incredibly narrow, like recognizing yes, no and digits.

And then it slowly expanded.

It was nothing.

That's where you started?

Yeah, that was where I started.

Yes, no, tell me yes or no was the model.

It was yes, no digits.

Very, very basic stuff.

Nothing like the kind of, you know, typing with your voice that's been available on smartphones now where you can just push a microphone, talk and it all just appears as a transcription, let alone actually understanding what that says and conducting a conversation.

And so there were very brittle models that would be good within a domain, but if you tiptoed even slightly outside that domain, they'd break.

What's happened now with this, this generative AI?

It's a revolution, it's a, it's a, it's a different, it's actually a different implementation.

They're different technical models, they're running on mind boggling levels of compute.

So just the absolute scale is vastly, vastly different and incomprehensibly bigger.

And I mean that word.

I think most people, including me, actually can't compre how big and sophisticated the models that exist today are.

So there's even a failure of imagination in using them.

And it is night and day.

The old school ML machine learning and data models are still very effective and very good at certain things.

And sometimes they're the best use.

They actually have discrete use cases that are different to the GPT and transformer based technologies that have taken over, but it's just a different world.

And for someone who grew up in speech and language technology, being able to have a completely natural, you know, 40 minute conversation with ChatGPT, totally natural, totally real time on any topic and just for giggles, switch language halfway through.

It's crazy to me that that is now a free feature in technology that's available to absolutely everybody.

That would have been unfathomable.

And the other thing that's striking to me is things that would have basically have us accusing people of witchcraft a year ago are viewed as mundane now.

It's like, oh that, yeah, yeah, you can insert yourself into a photorealistic video.

Yeah, well I can do that actually on my free subscription.

It's crazy.

And we're all basically catching up to the technology.

How has AI changed the way you lead?

Three main ways.

Sorry, I had to kind of do a quick.

So personally I use it a lot.

It's incredibly helpful.

There's kind of just kicking off research, having like Socratic dialogues with, with AI to kind of explore ideas, pressure test ideas, flesh ideas out.

I do that a lot on a personal basis and I use it in my work.

We use it inside the company a lot.

I think there are opportunities for genuine improvements in both productivity and efficiency.

I think there's an opportunity to automate a lot of pretty mundane stuff.

I think that's going to be a rocky road because there's going to be a lot of false starts.

There's going to be a lot of ROIs that don't quite seem to show up, but we'll get.

I think it's going to take time, but I do think there's going to be a fairly transformational level of automation that we can glean from AI in the workplace.

And then the third track is it's hugely influenced our product roadmap in a very short space.

It's gone from, we've gone from saying, what is this GPT stuff, you know, these GPT models, to getting AI features inside our products, to having AI native agentic products.

And that has happened at breakneck speed.

And I mean, who knows what's going to be possible in the next 12 months.

And we kind of advise our product leaders to be thinking, design the product for what you think will be achievable in 2027 because you'll be able to do it by the end of 2026 or probably middle.

I like that.

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Where are leaders overestimating AI capabilities and where are they underestimating AI capabilities now?

I think there is an overestimation of its ability to abstract in the way that you want it to abstract.

So I'll give you an example that's kind of in our domain of software development, there's a set of tools that are called vibe coding tools where you can give a very, very high level instruction to a product and say, build me a fitness tracker.

And it will, it will build you a fitness tracker.

But what happens is it says, okay, here it is.

And you go, oh, I didn't mean like that.

I wanted it to actually have a different look and feel.

And I did.

I need, I actually wanted a calorie counter in there as well and I didn't want it to.

So because you've just given it this very, very high level instruction, you've given it the ability to make a whole load of decisions on its own.

And if you then don't like the decisions it's made, it's very difficult to unwind.

And so there's this fallacy that's emerged that anybody can be a software developer.

That isn't true.

The only people who are really getting huge productivity benefits out of these tools are people who are software developers already.

You have to understand how software works, you have to understand how the architectures work.

And so there's real misconceptions around that.

And I think you can generalize that to people thinking, oh, I'm just going to be able to have agents do my expense approvals and my talent acquisition strategy and all sorts of other things.

And the answer to that is, sorry, my, my warning there would be be very careful because those are things that you shouldn't let it decide on its own.

And if you're going to have it learn, you better make your process, make sure your processes are very good or it's going to just learn off your bad processes and automate those.

So we'll do bad things really quickly.

Because it carries context.

So if you give it a really big high level instruction, it generates all the context and then you're asking for iterations upon that.

And like you said, unwinding it is like a giant spider web.

You have to just start from scratch over again.

Right?

So better to start with a deep knowledge and giving it a lot of instruction so it builds the right context and doesn't learn the errors from itself.

And weirdly, I think the way people underestimate it on a very practical level today is there's certain tasks where you do want to leave some room for creativity.

If you try and over specify the task, you won't leverage the full capability of the models.

So you've got to find that right balance where you're giving it enough context but you're not controlling it too much or else you'll constrain itself sort of natural.

I'd say natural, but you know what I mean, it's native intelligence too much for it to actually give you the best result that you could get.

And I think what makes that even harder for everybody is the state of the art is evolving so quickly that you get a leap forward in technology.

We all start calibrating to that new standard of what's possible.

And before we really got there, it leaps forward again.

And so do you now jump to the new one and start calibrating to that, or do you stay where you are and become a real expert in an earlier generation of the state of the art?

And I think people are still trying to figure out where they should fall on that spectrum.

Like, we made a decision pretty early that we're going to standardize on a couple of the frontier models and we're just going to say, look, yep, sometimes they won't be the best and sometimes they will be, and we'll live with that, but we're going to get very good at using these models.

We're kind of placing our bets on those for now.

And I think that's benefited from us because if you're just hopping around too much, you'll never get expert enough in any of them.

Someone told me, ben, the real world of AI is like a high stakes casino.

Every bet, ivory bet is big and every bag.

Yeah, it's, it's a, it's a wild thing.

And I'm so glad that we've got thoughtful leaders like you at the helm of these organizations helping, helping do that wisely.

I'd like you to, for a second to speak to two different groups of people.

One is high school freshman and one is college freshman.

What is your career advice for what they should do to prepare, whether it's like class orientation or things to pursue outside of work.

But I'm looking at that as like people who are trying to plan their college career and those that are before that trying to plan their high school career and to really be in a position four years later, eight years later, to take advantage of the opportunities that are out there.

If you're leaving high school now, I would only select colleges that have embraced AI in the curriculum and encourage the use of AI throughout the courses.

I think it's extremely counterproductive for a college not to embrace that and not to prepare people to use AI as fulsomely as they possibly can.

And I, I, I got a pretty high degree of conviction about that.

I actually think you're putting, a college would be putting its students at a disadvantage if it doesn't.

So, so if you're leaving high school and thinking about going to college, pick your colleges according to ones there.

What are the green lights on colleges now?

Like you would know, hey, if they're doing.

Because these students, they think they're like, they show up, they walk through the campus, you know, what are they looking for?

Because they're only getting sort of the marketing pitch for the college versus really what's going on.

So what can you do to sort of thinly slice the science?

You're talking to someone whose daughter is doing this right now, walking through the campuses.

She's literally on a college visit, as we're talking about.

So look at the courses.

It's very easy to find the curricula online.

It's very easy to actually ask admissions staff what the philosophy is on AI.

They should have a position.

And if they don't have a position that they can explain to you, then I would be cautious for people who are.

Were you saying a high school freshman and a college freshman or a graduate and a graduate?

I think I might have gone right.

I'm thinking about kids planning their career, planning their next four years.

So when you're in college, you got a little more flexibility and you're trying to pick that college and maybe.

So then we'll go, we'll go back to high school.

But maybe now take the kid who is four years, you know, they've been in college, they're about to come out of college, they're reading the Wall Street Journal or freaking out because they're saying it's a terrible market for college grads and there are no jobs unless you're like an AI whiz kid, which surprise to them.

So I would again say become as adept at using AI tools as you possibly can, even if it's just getting really good with ChatGPT or Claude or Gemini or any of these kind of open, open, freely available ones.

They're not open source, but freely available models so that you can talk about that literacy, demonstrate that literacy to employers.

I just think that candidates who are completely inexperienced with using AI are going to be less employable.

Candidates who say, I get it, I understand what it's good at.

I understand kind of where the boundaries of the current state of the art are between what it can do and what I can do uniquely, those are going to be the people who flourish.

Because I think everybody is going to have to embrace technology and embrace the advance of this technology to be successful in their careers.

It will be the people who resist it, ignore it, that are just going to find that people are not that, that interested in employing them.

Yeah, I love that.

So, like these college kids coming out, demonstrate projects, speak about AI, can, can add to the value of the organization, like, do your homework, be thoughtful and be creative.

And so I'm thinking about like, take care what you're saying, showing up and be able to speak to it specifically, because that's a value that you can add that maybe the company doesn't have right now.

Yes.

And you don't have to be an AI wiz.

I would say if you're going into finance and you can show how your.

Even if it's like, hey, I use the co pilot features embedded in Excel to do analysis and to help me speed up my tasks and give me insights I wouldn't have achieved otherwise, I'm using any of these content creation tools that are available to marketers to generate content, spin up different types of content.

And it's not just speed, it's speed plus quality.

And if you can show that you're curious about that.

That's a word that keeps coming up, right?

Curious and willing to experiment and you've done some stuff on your own, you are just going to look more attractive.

And it applies to finance, HR development, every, every function.

Very, very good.

All right, so let's now let's wind the clock back and I kind of like beginning at the end, applying to jobs now you're going back through college, all the way back to high school.

So in high school these days, you know, they've got a couple options maybe every year on what they can take, but not a lot of options.

What's your advice to high school freshmen whose school even may be on the fence about or even unsure and this decision of, well, how much AI access do we have them use?

If they become reliant upon it, then, you know, like they miss some like learning fundamentals perhaps.

But if they stone cold shut out of AI entirely, they're not keeping up with the rest of the world.

So how.

What's your advice to them?

My advice to a high school freshman, first of all, don't cheat.

Be honest.

If you're asked to do something yourself, do it yourself.

Yes.

And go and learn as much as you can about AI.

Use it.

It's really similar advice that I'd give to the college freshman is whether or not your school is encouraging you to use it.

And let's say you're at a school that really discourages it, that somehow views using it cheating or plagiarism, which I don't think it should.

I think as long as you're clear, it's like I used AI to help me with this or this bit was created by AI I think it's very clear that you're kind of collaborating with AI, which is how we're all going to work.

But even if you had a school that was relatively draconian and said, no.

AI.

Go use it outside, it's actually pretty good fun.

You can do a lot with it.

It doesn't cost you anything.

There's really good stuff that's completely free.

And I.

There's just so much you can do.

And not just work stuff.

You can do it for fun stuff.

Just use it.

It's the best advice I would give the high school freshmen to love that.

I.

We had our back to school night.

My daughter happens to be a high school freshman.

And I asked the teacher, I'm like, well, how are you all going to use AI?

Like, are you.

Are you just totally against it?

And she said, they're going to have some activities where they use it.

She's like, but I can't really prevent them from using it other than in class.

She's like, what I tell them is, the intent of our class is for you to find your voice.

And if you're letting a machine do all the writing, then what's the point of us being here?

And I thought, wow, that's a great response.

It's like, what is the intent of your class?

And if it is to find your voice, there are maybe some AI tools to help with that.

But you also got to do the heavy lifting yourself.

And, you know, just having some ways to define that for all different disciplines in school is going to be so important.

And I think I agree with that, Ben.

I think that one of the most valuable skills anybody can take with them through life is the ability to think for yourself.

It's very easy to go along with the flow, to just accept things as fact without thinking through them.

By the way, that's not a do your own research is the world really round kind of thing.

It's come to your own conclusions rather than just going along with the herd.

And that requires critical thinking.

And I think that's one of the most important skills that you can, that you can learn at school.

And if that's what the teacher meant by finding your own voice, then then I completely agree.

Next time you talk to that teacher, I think you should ask her if she's used AI to help create lesson plans or help with grading.

And I suspect the answer will be yes.

Yes.

There you go.

So it's a constant dialogue, you know.

Yeah.

In terms of how we do this.

And it requires.

I think it's inevitable.

So you are trying to hold back a tide if you're avoiding using it and rather than saying, let's not use it.

I bet if you sat down and spent a few hours brainstorming and said, well, how could we incorporate AI into this class knowing everyone has access to it?

Given that our goal is for people to discover their own voice, you'd probably end up in a pretty good spot.

Why not create your own GPT for your own lesson plans that integrates and helps them walk through a series of prompts to help discover their voice, you know, their writing voice.

So there's a lot of man, it's exciting and that's scary.

All right, winding this up here.

So in your personal life, what have you found the most fun or productive or enjoyable way to use AI?

I got the bug very quickly when these new models emerged.

And so one of the things I did when Midjourney first came out, I not quite sure why I did this, but I created a unique deck of playing cards with like a custom designed deck of playing cards using Mid Journey and then found a site that would like custom print playing cards and made them and distributed to my friends.

Wait, Midjourney is.

What is that like Dungeons and Dragons is.

What is a big.

They're one of the best known image generation AI services.

Okay, so that, that was, that was fun.

And it.

So I will use it for all sorts.

I'll create songs, storybooks, movies.

I use AI a lot outside of work as well.

I, I'm just endlessly fascinated by it.

So this.

Did this car, is it a card game or, or is it like their traditional cars?

The different design or.

Yeah, different designs with and unique pictures on the back.

I love that.

Yeah, a true AI leader because you're using your personal life and professional life.

Last question.

When you're using it in your personal life, what is a red flag in terms of, you know, there's a mistake?

Like what is there.

Is there a telltale sign you're using AI and you're like, this is just.

Now I will borrow an example that I saw recently from, from a video of someone who's doing some pretty amazing stuff with something, an area called context engineering, which is really kind of the right way to use AI in software development.

And he said, you know, things have really gone off the rails when the AI prompt begins with, you're absolutely right.

That's when it's, that's when it's raising the right flag and say, you know what?

I completely screwed that up.

Oh, Dan, this has been such a fun one.

It's a great.

I mean, what a career journey.

And you sound like you've put so much thought into this on AI from all different angles and so helpful for our leaders today.

Thanks for coming on the show, sir.

Thank you Ben.

It's been fun.

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